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generation/2d_vqgan/2d_vqgan_tutorial.ipynb

@@ -624,7 +624,7 @@
     "val_samples = np.linspace(val_interval, max_epochs, int(max_epochs / val_interval))\n",
     "num_inters = min(len(intermediary_images), 4)\n",
     "fig, ax = plt.subplots(nrows=num_inters, ncols=1, sharey=True)\n",
-    "ax=ensure_tuple(ax)\n",
+    "ax = ensure_tuple(ax)\n",
     "for image_n in range(num_inters):\n",
     "    reconstructions = torch.reshape(intermediary_images[image_n], (64 * n_example_images, 64)).T\n",
     "    ax[image_n].imshow(reconstructions.cpu(), cmap=\"gray\")\n",